This project aims to analyze and predict housing prices in Amsterdam using comprehensive data collected in August 2021. The Amsterdam housing market has experienced significant fluctuations in recent years, driven by various factors such as economic conditions, demographics, and housing policies. Understanding the dynamics of this market is crucial for buyers, sellers, and investors alike. Therefore, the primary objective of this analysis is to identify and comprehend the trends that influence housing prices in Amsterdam. By examining a rich dataset that includes detailed information about house prices and their associated features, we seek to uncover variables that exhibit a strong correlation with housing prices.
In our exploration of the dataset, we will focus on identifying key predictors of housing prices. These predictors may include various attributes such as the area of the property, the number of rooms, the location’s longitude and latitude, and other relevant features. Through exploratory data analysis (EDA), we will visualize these relationships to determine which factors most significantly impact housing prices.
Furthermore, the project will assess whether the identified predictors can be effectively employed in a predictive model to estimate housing prices and forecast market trends. By leveraging statistical techniques and machine learning algorithms, we aim to develop a robust model that offers a good fit for predicting housing prices based on the available dataset. This model will not only facilitate a deeper understanding of how various factors interact and contribute to price fluctuations but also provide practical applications for stakeholders in the real estate market.
This dataset detailed information about house prices in Amsterdam, Netherlands as of August 2021.
The housing prices have been obtained from Pararius.nl as a snapshot in August 2021. The original data provided features such as price, floor area and the number of rooms. The data has been further enhanced by utilising the Mapbox API to obtain the coordinates of each listing.
The Amsterdam House Price Prediction data set contains 924 records and includes features such as Address, Zip, Rooms, Area, Lat, Lon and Price as defined below. However, there are 4 missing values in the “Price” field. To ensure data integrity, these records with missing prices are removed before further analysis or modeling. This step ensures accurate predictions by eliminating incomplete data points, which could skew the results of the machine learning models designed to predict house prices based on the remaining features.
The 7 for sale in and around Amsterdam as in the data set are:
Address : Residential address
Zip : Residential Zip code.
Price : Residential price in Euros.
Area : Residential area of House in square meters.
Room : Number of rooms in house
Lon : Longitude coordinates of location of house
Lat : Latitude coordinates of location of house
## X Address Zip Price Area Room Lon Lat
## 1 1 . .. ... ..... ..... 1091 CR 685000 64 3 4.907736 52.35616
## 2 2 . .. ... ..... ..... 1059 EL 475000 60 3 4.850476 52.34859
## 3 3 . .. ... ..... ..... 1097 SM 850000 109 4 4.944774 52.34378
## 4 4 . .. ... ..... ..... 1060 TH 580000 128 6 4.789928 52.34371
## 5 5 . .. ... ..... ..... 1036 KN 720000 138 5 4.902503 52.41054
## 6 6 . .. ... ..... ..... 1051 AM 450000 53 2 4.875024 52.38223
## Price Area Rooms Longitude Latitude
## Price 1.00000000 0.83509018 0.62344800 -0.01356113 0.06219568
## Area 0.83509018 1.00000000 0.80828526 0.02176190 0.01417911
## Rooms 0.62344800 0.80828526 1.00000000 -0.02575327 -0.02116819
## Longitude -0.01356113 0.02176190 -0.02575327 1.00000000 -0.18344478
## Latitude 0.06219568 0.01417911 -0.02116819 -0.18344478 1.00000000
The exploratory data analysis (EDA) conducted on the Amsterdam housing dataset provides valuable insights into the factors influencing housing prices. The key findings from the EDA are summarized below:
This project successfully analyzed and predicted housing prices in Amsterdam by examining a comprehensive dataset from August 2021. Through univariate, bivariate, and multivariate data analyses, we identified significant trends and correlations between various predictors such as area, number of rooms, and geographic location.
Rooms and Area vs Price: House price is strongly positvely influenced by area and the number of rooms, as seen in the various bivariate and multivariate plots. so, in prediction it will get more weight.
Latitude and Longitude vs Price: longitude and latitude, also affects house prices, though not as strongly as the size of the home. So, they will play a role in prediction but will have less weight.
Correlation of Variables: The correlation heatmap and pair plots reinforce the findings that area and room count are the strongest predictors of price, with location playing a secondary role.
The strong positive correlation between the Area and
Price, as well as the moderate correlation with
Rooms, indicates that a linear relationship may exist
between these variables and house prices. This suggests that a linear
regression model could provide a solid baseline for predictions. Also,
other predictors identified during the EDA , such as
Longitude and Latitude, can be included as
independent variables in the linear model. These features have shown
significant relationships with house prices and are likely to contribute
meaningfully to the model’s accuracy.